{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = load_wine() # 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.utils.Bunch'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(wine))# 字典格式；保存了数据集及相关元数据\n",
    "wine.data.shape # wine.data：输入（特征）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])\n"
     ]
    }
   ],
   "source": [
    "print(wine.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target # 标签；三分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2     3      4     5     6     7     8      9     10  \\\n",
       "0    14.23  1.71  2.43  15.6  127.0  2.80  3.06  0.28  2.29   5.64  1.04   \n",
       "1    13.20  1.78  2.14  11.2  100.0  2.65  2.76  0.26  1.28   4.38  1.05   \n",
       "2    13.16  2.36  2.67  18.6  101.0  2.80  3.24  0.30  2.81   5.68  1.03   \n",
       "3    14.37  1.95  2.50  16.8  113.0  3.85  3.49  0.24  2.18   7.80  0.86   \n",
       "4    13.24  2.59  2.87  21.0  118.0  2.80  2.69  0.39  1.82   4.32  1.04   \n",
       "..     ...   ...   ...   ...    ...   ...   ...   ...   ...    ...   ...   \n",
       "173  13.71  5.65  2.45  20.5   95.0  1.68  0.61  0.52  1.06   7.70  0.64   \n",
       "174  13.40  3.91  2.48  23.0  102.0  1.80  0.75  0.43  1.41   7.30  0.70   \n",
       "175  13.27  4.28  2.26  20.0  120.0  1.59  0.69  0.43  1.35  10.20  0.59   \n",
       "176  13.17  2.59  2.37  20.0  120.0  1.65  0.68  0.53  1.46   9.30  0.60   \n",
       "177  14.13  4.10  2.74  24.5   96.0  2.05  0.76  0.56  1.35   9.20  0.61   \n",
       "\n",
       "       11      12  0   \n",
       "0    3.92  1065.0   0  \n",
       "1    3.40  1050.0   0  \n",
       "2    3.17  1185.0   0  \n",
       "3    3.45  1480.0   0  \n",
       "4    2.93   735.0   0  \n",
       "..    ...     ...  ..  \n",
       "173  1.74   740.0   2  \n",
       "174  1.56   750.0   2  \n",
       "175  1.56   835.0   2  \n",
       "176  1.62   840.0   2  \n",
       "177  1.60   560.0   2  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 13)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ytrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9444444444444444"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sklearn的实现是通过随机特征选取来生成多棵树实现集成\n",
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\") # criterion:默认是gini\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score = clf.score(Xtest, Ytest) #返回预测的准确度accuracy\n",
    "\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = ['酒精','苹果酸','灰','灰的碱性','镁','总酚','类黄酮','非黄烷类酚类','花青素','颜色强度','色调','od280/od315稀释葡萄酒','脯氨酸']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.02457256, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.45799767, 0.        , 0.        , 0.22130544,\n",
       "       0.        , 0.        , 0.29612433])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_  # 这是基于随机森林的吗?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zip"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[*zip(feature_name,clf.feature_importances_)]\n",
    "#list(zip(feature_name,clf.feature_importances_)) # 两种写法等价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9814814814814815"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=30)\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score = clf.score(Xtest, Ytest) #返回预测的准确度\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "splitter用来控制决策树生成规则，输入”best\"，决策树在分枝时虽然随机，但是还是会\n",
    "优先选择更重要的特征进行分枝（重要性可以通过属性feature_importances_查看），输入“random\"，决策树在\n",
    "分枝时会更加随机，树会因为含有更多的不必要信息而更深更大，并因这些不必要信息而降低对训练集的拟合。这\n",
    "也是防止过拟合的一种方式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9814814814814815"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\"\n",
    "                                    ,random_state=30\n",
    "                                    ,splitter=\"random\"\n",
    "                                    )\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score = clf.score(Xtest, Ytest)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 剪枝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#我们的树对训练集的拟合程度如何？\n",
    "score_train = clf.score(Xtrain, Ytrain)\n",
    "score_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_samples_leaf:一个节点在分枝后的每个子节点都必须包含至少min_samples_leaf个训练样本，否则分枝就不会发生，或者，分枝会朝着满足每个子节点都包含min_samples_leaf个样本的方向去发生.一般搭配max_depth使用，在回归树中有神奇的效果，可以让模型变得更加平滑。这个参数的数量设置得太小会引起过拟合，设置得太大就会欠拟合。一般来说，建议从=5开始使用。如果叶节点中含有的样本量变化很大，建议输入**浮点数**作为样本量的百分比来使用。同时，这个参数可以保证每个叶子的最小尺寸，可以在回归问题中避免低方差，过拟合的叶子节点出现。对于类别不多的分类问题，=1通常就是最佳选择。\\\n",
    "min_samples_split:一个节点必须要包含至少min_samples_split个训练样本，这个节点才允许被分枝，否则\n",
    "分枝就不会发生."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\"\n",
    "                                    ,random_state=30\n",
    "                                    ,splitter=\"random\"\n",
    "                                    ,max_depth=3  # 最大树深\n",
    "                                    ,min_samples_leaf=10\n",
    "                                    ,min_samples_split=25\n",
    "                                    )\n",
    "clf = clf.fit(Xtrain, Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = clf.score(Xtest, Ytest)\n",
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**max_features**:max_features限制生成决策树过程中用到的特征个数，超过限制个数的特征都会被舍弃。和max_depth异曲同工，max_features是用来限制高维度数据的过拟合的剪枝参数，但其方法比较暴力，是直接限制可以使用的特征数量而强行使决策树停下的参数，在不知道决策树中的各个特征的重要性的情况下，强行设定这个参数可能会导致模型学习不足。如果希望通过降维的方式防止过拟合，建议使用PCA，ICA或者特征选择模块中的降维算法。\n",
    "\n",
    "**min_impurity_decrease**:限制信息增益的大小，信息增益小于设定数值的分枝不会发生。这是在0.19版本中更新的功能，在0.19版本之前时使用min_impurity_split。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**class_weight** :处理样本不平衡.class_weight参数对样本标签进行一定的均衡，给少量的标签更多的权重，让模型更偏向少数类，向捕获少数类的方向建模。该参数默认None，此模式表示自动给与数据集中的所有标签相同的权重。\n",
    "\n",
    "**min_weight_fraction_leaf**:有了权重之后，样本量就不再是单纯地记录数目，而是受输入的权重影响了，因此这时候剪枝，就需要搭配min_weight_fraction_leaf这个基于权重的剪枝参数来使用。另请注意，基于权重的剪枝参数（例如min_weight_fraction_leaf）将比不知道样本权重的标准（比如min_samples_leaf）更少偏向主导类。如果样本是加权的，则使用基于权重的预修剪标准更容易优化树结构，这确保叶节点至少包含样本权重的总和的一小部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "test = []\n",
    "for i in range(10):\n",
    "    clf = tree.DecisionTreeClassifier(max_depth=i+1\n",
    "                                    ,criterion=\"entropy\"\n",
    "                                    ,random_state=30\n",
    "                                    ,splitter=\"random\"\n",
    "                                    )\n",
    "    clf = clf.fit(Xtrain, Ytrain)\n",
    "    score = clf.score(Xtest, Ytest)\n",
    "    test.append(score)\n",
    "plt.plot(range(1,11),test,color=\"red\",label=\"max_depth\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一些接口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 27, 19, 27,  7,  7,  7, 27, 19, 27, 21, 21,  7, 18,  7, 21,  7,\n",
       "       10,  7, 19,  7, 27, 19, 19, 27, 21,  7, 21, 27,  7, 19,  7,  7, 27,\n",
       "       21, 27, 27,  7, 27,  7, 19, 27,  7, 27, 27, 19, 19, 10, 27, 27, 27,\n",
       "       10,  7, 21], dtype=int64)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#apply返回每个测试样本所在的叶子节点的索引\n",
    "clf.apply(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 2, 2, 2, 0, 1, 0, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0,\n",
       "       1, 1, 0, 1, 2, 1, 0, 2, 1, 2, 2, 0, 1, 0, 0, 2, 0, 2, 1, 0, 2, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 2, 1])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict返回每个测试样本的分类/回归结果\n",
    "clf.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"float: left;\" src=\"interfaces.png\" width=\"50%\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一些属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.tree._tree.Tree at 0x1ae7a920ce0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.classes_ # 输出所有类标签\n",
    "clf.max_features_ # 参数max_features的推断值\n",
    "clf.n_classes_  # 类别的个数\n",
    "clf.n_features_in_  # 训练时使用的特征个数\n",
    "clf.n_outputs_  # 训练时输出结果个数\n",
    "clf.tree_ # 返回一个建好的树的端口,通过端口访问树的结构和低级属性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 回归树"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "class sklearn.tree.DecisionTreeRegressor (\n",
    "criterion=’mse’, splitter=’best’, \n",
    "max_depth=None, min_samples_split=2, \n",
    "min_samples_leaf=1, min_weight_fraction_leaf=0.0, \n",
    "max_features=None, random_state=None, \n",
    "max_leaf_nodes=None, min_impurity_decrease=0.0, \n",
    "min_impurity_split=None, presort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "citerion主要有三个:MSE, friedman, MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\torch1.8\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "    \n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.target[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scoring: 评价指标默认为R平方\\\n",
    "    --\"neg_mean_squared_error\": 返回负均方误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-18.08941176, -10.61843137, -16.31843137, -44.97803922,\n",
       "       -17.12509804, -49.71509804, -12.9986    , -88.4514    ,\n",
       "       -55.7914    , -25.0816    ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "# 交叉验证\n",
    "cross_val_score(regressor, boston.data, boston.target, cv=10,\n",
    "                scoring = \"neg_mean_squared_error\"\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(1) \n",
    "X = np.sort(5 * rng.rand(80,1), axis=0) #生成0~5之间随机的x的取值\n",
    "y = np.sin(X).ravel() # ravel()：将多维矩阵展平为1维\n",
    "y[::5] += 3 * (0.5 - rng.rand(16)) #在正弦曲线上加噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1ec9d7b3b80>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(X, y, s=20, edgecolor=\"black\",c=\"darkorange\", label=\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_1 = DecisionTreeRegressor(max_depth=2)\n",
    "regr_2 = DecisionTreeRegressor(max_depth=5)\n",
    "regr_1.fit(X, y)\n",
    "regr_2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = regr_1.predict(X_test)\n",
    "y_2 = regr_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(X, y, s=20, edgecolor=\"black\",c=\"darkorange\", label=\"data\")\n",
    "plt.plot(X_test, y_1, color=\"cornflowerblue\",label=\"max_depth=2\", linewidth=2)\n",
    "plt.plot(X_test, y_2, color=\"yellowgreen\", label=\"max_depth=5\", linewidth=2)\n",
    "plt.xlabel(\"data\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.title(\"Decision Tree Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 泰坦尼克号幸存者预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"data.csv\",index_col = 0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Survived</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>35.0</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "PassengerId                                                              \n",
       "1                   0       3    1  22.0      1      0   7.2500         0\n",
       "2                   1       1    0  38.0      1      0  71.2833         1\n",
       "3                   1       3    0  26.0      0      0   7.9250         0\n",
       "4                   1       1    0  35.0      1      0  53.1000         0\n",
       "5                   0       3    1  35.0      0      0   8.0500         0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除缺失值过多的列和无关的列\n",
    "data.drop([\"Cabin\",\"Name\",\"Ticket\"], inplace=True, axis=1) # axis指示行或列\n",
    "\n",
    "# 缺失值填补\n",
    "data[\"Age\"] = data[\"Age\"].fillna(data[\"Age\"].mean())\n",
    "# 直接删除存在缺失值的行\n",
    "data = data.dropna()\n",
    "\n",
    "# 将二值特征转为数值0，1\n",
    "data[\"Sex\"] = (data[\"Sex\"]== \"male\").astype(\"int\")\n",
    "\n",
    "#将三值特征转换为数值\n",
    "labels = data[\"Embarked\"].unique().tolist() # ['S', 'C', 'Q']\n",
    "data[\"Embarked\"] = data[\"Embarked\"].apply(lambda x: labels.index(x)) #['S', 'C', 'Q']->[0, 1, 2]\n",
    "\n",
    "#查看处理后的数据集\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,data.columns != \"Survived\"]\n",
    "y = data.iloc[:,data.columns == \"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
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       "      <td>3</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
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       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass  Sex        Age  SibSp  Parch      Fare  Embarked\n",
       "PassengerId                                                          \n",
       "859               3    0  24.000000      0      3   19.2583         1\n",
       "333               1    1  38.000000      0      1  153.4625         0\n",
       "101               3    0  28.000000      0      0    7.8958         0\n",
       "471               3    1  29.699118      0      0    7.2500         0\n",
       "49                3    1  29.699118      2      0   21.6792         1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.8500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.4000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
       "0       3    1  30.000000      1      0  16.1000         0\n",
       "1       1    0  26.000000      0      0  78.8500         0\n",
       "2       3    1  29.699118      0      0  14.5000         0\n",
       "3       2    0  14.000000      1      0  30.0708         1\n",
       "4       1    1  29.699118      0      0  42.4000         0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重置索引标签\n",
    "for i in [Xtrain, Xtest, Ytrain, Ytest]:\n",
    "    i.index = range(i.shape[0])\n",
    "    \n",
    "#查看分好的训练集和测试集\n",
    "Xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7265917602996255\n",
      "0.7739274770173645\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=25)\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score_ = clf.score(Xtest, Ytest)\n",
    "\n",
    "print(score_)\n",
    "\n",
    "score = cross_val_score(clf,X,y,cv=10).mean()\n",
    "\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8177860061287026\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tr = []\n",
    "te = []\n",
    "for i in range(10):\n",
    "    clf = DecisionTreeClassifier(random_state=25\n",
    "                                 ,max_depth=i+1\n",
    "                                 ,criterion=\"entropy\"\n",
    "                                )\n",
    "    clf = clf.fit(Xtrain, Ytrain)\n",
    "    score_tr = clf.score(Xtrain,Ytrain)\n",
    "    score_te = cross_val_score(clf,X,y,cv=10).mean()\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "print(max(te))\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.plot(range(1,11),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(range(1,11),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(range(1,11))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**用网格搜索确定参数**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8280337941628264"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# gini_thresholds = [0, 0.5]\n",
    "# entropy_thresholds = [0, 1]\n",
    "parameters = {'splitter':('best','random')\n",
    "              ,'criterion':(\"gini\",\"entropy\")\n",
    "              ,\"max_depth\":[*range(1,10)]\n",
    "              ,'min_samples_leaf':[*range(1,50,5)]\n",
    "              ,'min_impurity_decrease':[*np.linspace(0,0.5,10)]\n",
    "             }\n",
    "\n",
    "clf = DecisionTreeClassifier(random_state=25)\n",
    "\n",
    "# GridSearchCV的fit()接口里面会执行交叉验证；CV指的就是交叉验证\n",
    "GS = GridSearchCV(clf, parameters, cv=10)\n",
    "GS.fit(Xtrain,Ytrain)\n",
    "\n",
    "print(GS.best_params_)\n",
    "\n",
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树分类示例(合成数据集)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "数据集生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (4,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2)\n",
      "(100,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make_classification库生成随机的二分型数据\n",
    "X, y = make_classification(n_samples=100, #生成100个样本\n",
    "                           n_features=2,  #包含2个特征，即生成二维数据\n",
    "                           n_redundant=0, #添加冗余特征0个\n",
    "                           n_informative=2, #包含信息的特征是2个\n",
    "                           random_state=1,  \n",
    "                           n_clusters_per_class=1 #每个簇内包含的标签类别有1个\n",
    "                         )\n",
    "print(X.shape)\n",
    "print(y.shape)\n",
    "# 因为X是二维特征，所以可以方便的可视化\n",
    "plt.scatter(X[:,0],X[:,1])\n",
    "\n",
    "# X的分布太明显，将混乱程度加大\n",
    "rng = np.random.RandomState(2) \n",
    "X += 2 * rng.uniform(size=X.shape) #加减0~1之间的随机数\n",
    "linearly_separable = (X, y) \n",
    "plt.scatter(X[:,0],X[:,1])\n",
    "\n",
    "#用make_moons创建月亮型数据，make_circles创建环形数据，并将三组数据打包起来放在列表datasets中\n",
    "datasets = [make_moons(noise=0.3, random_state=0),\n",
    "            make_circles(noise=0.2, factor=0.5, random_state=1),\n",
    "            linearly_separable]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练与可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X (28, 25)\n",
      "X (27, 25)\n",
      "X (29, 25)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(6, 9))\n",
    "i = 1 # 图像位置索引\n",
    "#开始迭代数据，对datasets中的数据进行for循环\n",
    "t = 0\n",
    "for ds_index, ds in enumerate(datasets):\n",
    "    \n",
    "    #对X中的数据进行标准化处理，然后分训练集和测试集\n",
    "    X, y = ds\n",
    "    X = StandardScaler().fit_transform(X)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42)\n",
    "    \n",
    "    #找出数据集中两个特征的最大值和最小值，创造一个比两个特征的区间本身更大一点的区间\n",
    "    x1_min, x1_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    x2_min, x2_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    \n",
    "    #用特征向量生成网格数据，网格数据，其实就相当于坐标轴上无数个点\n",
    "\n",
    "    #函数meshgrid：给定一组x和一组y，生成两者组成的二维网格坐标\n",
    "    #（实际上两个坐标值是分开输出的，将array1和array2按位置配对才是真正的网格坐标）\n",
    "    #生成的网格数据，用来绘制决策边界\n",
    "    #因为绘制决策边界的函数contourf要求输入的两个特征都必须是二维的\n",
    "    array1,array2 = np.meshgrid(np.arange(x1_min, x1_max, 0.2),\n",
    "                         np.arange(x2_min, x2_max, 0.2))\n",
    "\n",
    "    #接下来生成彩色画布\n",
    "    #用ListedColormap为画布创建颜色，#FF0000正红，#0000FF正蓝\n",
    "    cm = plt.cm.RdBu\n",
    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "    \n",
    "    #添加子图\n",
    "    ax = plt.subplot(len(datasets), 2, i)\n",
    "    \n",
    "    #有三个坐标系，但只需要在第一个坐标系上有标题\n",
    "    if ds_index == 0:\n",
    "        ax.set_title(\"Input data\")\n",
    "    \n",
    "    #将数据集的分布放到我们的坐标系上\n",
    "    #先放训练集\n",
    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train,\n",
    "               cmap=cm_bright,edgecolors='k')\n",
    "    #放测试集\n",
    "    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test,\n",
    "               cmap=cm_bright, alpha=0.6,edgecolors='k')\n",
    "    \n",
    "    \n",
    "    ax.set_xlim(array1.min(), array1.max())\n",
    "    ax.set_ylim(array2.min(), array2.max())\n",
    "    ax.set_xticks(()) # 无刻度\n",
    "    ax.set_yticks(())\n",
    "    # 下一张图\n",
    "    i += 1\n",
    "      \n",
    "    #############################从这里开始是决策树模型##########################\n",
    "    \n",
    "    # 这部分i的取值是2，4，6\n",
    "    ax = plt.subplot(len(datasets),2,i)\n",
    "    \n",
    "    # 构建决策树\n",
    "    clf = DecisionTreeClassifier(max_depth=5)\n",
    "    clf.fit(X_train, y_train)\n",
    "    score = clf.score(X_test, y_test)\n",
    "    \n",
    "    # 绘制决策边界，为此，我们将为网格中的每个点指定一种颜色[x1_min，x1_max] x [x2_min，x2_max]\n",
    "    # 分类树的接口，predict_proba，返回样本所对应的标签类概率。\n",
    "    # 如[0,1,0] 说明此样本是类别2的概率是100%；[0.2, 0.3, 0.5] 说明是类别1的概率是20%\n",
    "    #类概率是样本所在的叶节点中相同类的样本数量/叶节点中的样本总数量\n",
    "    \n",
    "    #np.c_将两个数组组合起来\n",
    "    #np.c_[array1.ravel(),array2.ravel()]: 就是一组网格坐标\n",
    "\n",
    "    Z = clf.predict_proba(np.c_[array1.ravel(),array2.ravel()])\n",
    "    Z = Z[:, 1]\n",
    "    \n",
    "    #将返回的类概率作为数据，放到contourf里面绘制去绘制轮廓\n",
    "    Z = Z.reshape(array1.shape)\n",
    "    ax.contourf(array1, array2, Z, cmap=cm, alpha=.8)\n",
    "    \n",
    "    #将数据集的分布放到我们的坐标系上\n",
    "    # 将训练集放到图中去\n",
    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,\n",
    "               edgecolors='k')\n",
    "    # 将测试集放到图中去\n",
    "    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n",
    "               edgecolors='k', alpha=0.6)\n",
    "    \n",
    "\n",
    "    ax.set_xlim(array1.min(), array1.max())\n",
    "    ax.set_ylim(array2.min(), array2.max())\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    \n",
    "    #只需要在第一张图设置标题\n",
    "    if ds_index == 0:\n",
    "        ax.set_title(\"Decision Tree\")\n",
    "    \n",
    "    #概率写在图中    \n",
    "    ax.text(array1.max() - .3, array2.min() + .3, ('{:.1f}%'.format(score*100)),\n",
    "            size=15, horizontalalignment='right')\n",
    "    \n",
    "    #让i继续加一\n",
    "    i += 1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch1.8",
   "language": "python",
   "name": "torch1.8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
